INDDIDML

INDDIDML is a declarative data description and document markup language designed to describe complex data structures and support automated generation of documents and interfaces. The language combines data modeling with embedded markup to express structural constraints, semantic annotations, and presentation hints, enabling a single source to be transformed into APIs, reports, documentation, or user interfaces.

The concept arose in early 2020s discussions among data architecture and content engineering communities. It exists primarily as proposals, experimental projects, and niche tooling; there is no widely recognized governing body or formal standardization program. Adoption remains limited to specific organizations and research groups exploring data lineage and dynamic documentation workflows.

INDDIDML uses a hierarchical, tag-based syntax with schemas for validation. It supports modular namespaces, versioning, and extensibility hooks. The standard tooling typically includes a parser, a validator, and transformers that emit formats such as JSON, XML, HTML, or specialized documentation. Metadata support covers provenance, licensing, and audience targeting. Transformations can be driven by templates or declarative rules, enabling dynamic content generation while preserving source definitions.

It is intended for scenarios where data descriptions must be machine-readable and human-understandable, such as data dictionaries, API schemas, technical documentation, and enterprise reporting. It aims to reduce duplication by coupling data definitions with their presentation in a single artifact.

See also: Data modeling, Markup language, Data interchange format, XML, JSON Schema.